1. Field of the Invention
The invention relates to a method for determining travel route data.
2. Discussion of the Prior Art
Methods for determining travel route data, especially within the framework of destination guidance or navigation of a vehicle, are known in principle and have been described in detail, for example, in W/O 89/02142. In this respect, in particular, traffic routes are maintained in a central control station by segment in a digital map, especially in a digital street map, wherein every segment represents a traffic route between two nodes, which can be intersections, junctions or the like, and is described by static or dymamic parameters. The static parameters essentially consist of structual features of the traffic route such as the type of road condition, or state of the road, number of lanes, speed limit and attributes such as curvy, steep ascents and descents. Moreover, it is known to asign stationary sensors to every segment, wherein dynamic parameters such as the quality of vehicles passing a segment per unit of time and their speed are detached by these sensors.
Further, it is known from German reference DE 195 25 291 to receive dynamic traffic data by means of appropriately outfitted test vehicles and to transmit this dynamic traffic data to a central control station.
Further, the dynamic parameters can be supplemented by weather information and temporary restrictions such as construction sites.
Prognoses about future traffic conditions in every segment which form the basis for the navigation of vehicles are derived in a known manner from the static and dynamic data collected in the control station by means of fundamental diagrams.
However, prognoses achieved in this way have the drawback that while a necessary travel time can be determined through a determined navigation along a plurality of segments based on the actual and prognosticated traffic data available in the control station, the navigated vehicle remains bound to the predetermined routing even when unforeseeable, obstructive events occur.
Further, every prognosis based on fundamental data is still inexact because, on principle, up-to-date dynamic parameters are left out of consideration. This effect is amplified in that analytically obtained mathematical relationships between the fundamental data are very complex and therefore very time-consuming with respect to computer processing.